Computational Models-Based Detection of Peripheral Malarial Parasites in Blood Smears

The most common human parasite as per the medical experts is the malarial disease, which is caused by a protozoan parasite, and Plasmodium falciparum, a common parasite in humans. A microscopist with expertise in malaria diagnosis must conduct this complex procedure to identify the stages of infection. This epidemic is an ongoing disease in some parts of the world, which is commonly found. A Kaggle repository was used to upload the data collected from the NIH portal. The dataset contains 27558 samples, of which 13779 samples carry parasites and 13779 samples do not. This paper focuses on two of the most common deep transfer learning methods. Unlike other feature extractors, VGG-19's fine-tuning and pretraining made it an ideal feature extractor. Several image classification models, including VGG-19, have been pretrained on larger datasets. Additionally, deep learning strategies based on pretrained models are proposed for detecting malarial parasite cases in the early stages, in addition to an accuracy rating of 98.34∗ 0.51%.


Introduction
e leading cause of infection in all parts of the world is malaria, a deadly disease. e rapid consumption and high mortality rate of this epidemic condition have been documented throughout history. e global death toll from malaria was estimated at 4,29,000. In 2015, there were an estimated 3,03,000 children under the age of five years, and prenatal women are the most at risk of death [1]. By detecting this disease at an early stage, the death rate can be reduced and prevented. Researchers face a challenge in providing the most accurate parasite detection in the shortest amount of time, cost, and effort. During the last few decades, this visual inspection has played a vital role in being a tool in the health check field for decision making. A thick blood smear can identify malaria parasites in blood samples [2]. is shows that it is close to eleven times more sensitive than a thin blood smear for the rapid detection of parasites. It is often used to test the development stages to create blood smears placed on a microscope glass slide. To confirm malaria infection, a pathologist uses a light microscope to identify changes in the size, shape, and perception of various RBCs. e pathologist's understanding of the disease depends on the accuracy of a plasmodium microscopic report. is technique is arduous and inefficient due to uncertainty, which can cause erroneous and contradictory diagnoses, as well as inappropriate medication and, in rare situations, the death of the patient and specimens of infected and noninfected microbes.
A mechanism for detecting malaria infection has been proposed using supervised learning. e methods for detecting malaria parasites were demonstrated by using in vitro culture image samples [3]. e samples developed in a laboratory do not contain substances other than WBCs, platelets, or parasites. When considering the severity of malaria depending on the number of deaths caused by the disease, it is reasonable to accept possible minor errors caused by an automated method during execution [4,5]. Since the advent of deep learning techniques, feature extraction has been made far more efficient than traditional methods. Because deep learning methods still require trained experts and advanced techniques for calculating disease prediction, most of these methods still require efficient feature extraction optimization.
ere are many layers and levels of nonlinear mapping in CAD schemes based on ANN architecture. As a result of the layer-wise network of hidden layers, gradient-based optimization produces poor results [6,7]. Microscopic diagnosis requires extensive training, experience, and skills. In rural areas where malaria is prevalent, manual microscopy has not proven to be an effective screening tool when performed by nonexperts [8][9][10]. is model was intended to enhance the model's performance by modifying the network architecture and hyper-tuning the features to achieve a better-performing model. To determine the key features in the future, this paper focuses on the network architecture. e basic VGG-19 model obtains 85% accuracy, but after fine-tuning the model and applying the data augmentation technique to the training dataset, it can attain 97.14%. e following is the sequence under which this manuscript is structured. Section 2 represents the related work and data acquisition. Section 3 illustrates the proposed deep transfer learning methodology. Section 4 discusses the findings. Section 5 concludes the paper.

Related Work
ere are several methods of identifying parasite RBCs. Purwar et al. preprocessed by utilizing local histograms [11].
e Hough transform and morphological operations are used for segmentation and classification of diseased and clean cells. Di Ruberto et al. employed a statistical k-means clustering algorithm [12]. Using thresholding, Ritter and Cooper [13] have segmented cells, separated overlaps, and modified division lines according to Dijkstra's algorithm. Diaz et al. [14] applied efficiency to develop templates from parasite-stained images, which would then be used to classify each cell's infection life stages. RBCs can be segmented from the background in blood images using a variety of techniques. Díaz et al. [15] determined whether the RBCs were infected by the parasite from the host or not by separating them. Savkare et al. [16] and Ross et al. [17] analyzed grayscale images using k-means and k-medians to define the overall clusters into two parts. e significance of supervised learning is to identify classes of sample data. ese disease stages were identified with a blend of ML algorithms. Krizhevsky et al. [18] built a well-known CNN architecture, that is, the ALEX net, to compete with ImageNet and bagged awards. Quinn et al. [19] compared the CNN with a classifier using trees, and the accuracy rate was determined by analyzing the middle of the operating region. To identify blood cells on blood smear images, Chowdhury et al. [20] adopted a CNN approach to detect infections that were affected by the blood smears. Raviraja et al. [6] analyzed pretrained CNN models for malaria detection in blood cell images, namely, DenseNet121, VGG-16, Alexnet, ResNet50, FastAI, and ResNet101, Meng et al. [21]. With a high precision of 97.5%, ResNet50 excelled over the other CNN models [22]. To conclude, many deep learning algorithms for detecting malaria using cell images have been presented by Yue [23]. Many of them used large pre-trained CNN models to enhance the accuracy of classification, whereas others used customized CNNs to minimize the computational time [24]. e implications of substantial quantities of improperly classified data in medical image classification are catastrophic, and the objective of proposing a medical diagnosis tool is wrecked. In addition to efficiency, additional parameters such as F1 score [25,26], area under the curve (AUC) [27,28], sensitivity [29,30], and specificity [31][32][33] are vital in analyzing various approaches. A random sample of cell images infected/not infected with malaria is shown in Figure 1.

Data Acquisition.
e data was obtained from the National Institutes of Health portal and uploaded to a Kaggle repository. ere are 27558 cell images in the dataset, out of which 13779 are malaria-infected cell images and another 13779 are malaria-free cell images. e random samples were collected and separated into three sets: training, testing, and validation. Next, 8000 images were deployed for training and 3000 images for validation for each class. ese 11000 images have been used to train models. Subsequently, these were used along with the remaining 2779 images for each class as test data to evaluate the proposed models to perform on images that have never been seen before, because the original dataset included images with different dimensions, and it was scaled to equal dimensions before splitting the data. Most of the images were scaled to 128 × 128 pixels with three channels of RGB. Having all images equal will allow the neural networks to learn more quickly and with minimal mistakes in the future. e outcomes obtained excelled all existing methods, so data augmentation was used to improve the results. A few of the images in the class folders at the beginning are significantly different than the ones at the end. As a result, this was used to sample the data at random, as this will allow the proposed models to learn more diversified features from both classes, which will reduce overfitting and make our model more extensible to data.

Proposed Model
Morphological image processing is used to eliminate noise and recreate object features (see Figure 2). To achieve deep feature extraction and transfer learning, the convolutional layers are frozen. To classify RBCs, a fine-tuned pretrained CNN model with image augmentation is proposed (see Figure 3). e paths, according to K. Fukushima, are the beginning of a deep convolutional neural network architecture. e idea has been around for a while, but due to the lack of efficient high computational power, it has yet to gain momentum. In recent times, graphics processing units have advanced significantly toward high-performance computing technologies. Computational intelligence techniques have gained prominence as a result of their high possibility. CNN is the most popular type of this approach, which is composed of the layers described below.
ree main CNN layers were used in this algorithm, namely, "convolution," "pooling," and "fully connected layers." Figure 3    Contrast Media & Molecular Imaging cross-entropy. It has a learning rate of 0.01. In this case, the function gives a value between 0 and 1. Following the compilation of the dataset, the model will be trained based on the inputs received from the training samples, which will map the inputs to the outputs. It is a matter of choosing a set of weights that is best suited for resolving these problems.

Pretrained CNN Model.
is model was built from scratch using the original datasets that include all 19 trainable layers. e ReLU served as the activation function for the network. e Adam optimization was used to create the loss function. e model was built using transfer learning and using pre-trained frozen layers. Later, this model was used to generate the output of the images.
is was implemented using the sigmoid activation function as a simple feature extractor by freezing all five convolutional blocks to prevent the weights from moving across epochs.
is third model is fine-tuned and is built by freezing the first three blocks from the image net and then training blocks four and five from the malarial datasets. To fine-tune the VGG-model, blocks 4 and 5 were changed so that their weights are updated each time the model is evaluated. ese preprocessing strategies were applied to this model, which includes normalization, data augmentation, and standardization. A sigmoid activation function with two methods was applied to solve the classification problem to gain an output of 1 for infected and 0 for healthy.
encouraging outcomes. e basic VGG-19 model obtains 85% accuracy, but after fine-tuning the model and applying the data augmentation technique to the training dataset, it can attain 97.14%, as shown in Table 4. Transfer learning is a wonderful strategy that can be used to create promising results, according to the research and the performance analyses depicted in Table 5.

Conclusion
e deep learning neural network model was applied to improve the model's performance. It was shown that standardization and normalization had less impact on classification. e use of data augmentation improved the model performance and yielded positive results. Models of VGG-19 and ImageNet were derived from the initial concept using the combination of transfer learning and parameter tuning. To determine the key features, this paper has focused on the network architecture. is was intended to enhance the model's performance by modifying the network architecture and hyper-tuning the features to achieve a betterperforming model.

Data Availability
e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest
e authors declare that they have no conflicts of interest.